Energy systems conventionally rely upon forecasting to balance energy production with consumption. Accurate forecasts assist in an accurate balance between energy production and consumption and help to guarantee the stability of energy grids. When an energy system produces energy primarily with conventional energy sources, the energy production can be well matched to the energy consumption. With an increasing share of renewable energy sources, however, accurate forecasting is more challenging. Intermittent renewable energy sources such as wind and solar power can be subject to frequent and strong fluctuations, and their final power output can be difficult to predict beforehand. Energy storage capabilities are typically also rather limited, meaning that energy generated using intermittent renewable energy sources often must be directly used when available.
Forecasts are often calculated using mathematical models that capture a parameterized relationship between past and future values of time series data to express behavior and characteristics of a historic time series. Parameter value estimation for these forecast models is conventionally performed by applying an optimization algorithm and is typically very time consuming due to a parameter search space that increases exponentially with the number of model parameters.
Additionally, many energy systems exhibit a hierarchical data organization, with time series and forecast models on multiple levels. In such cases, the time series may be aggregated along the hierarchy based on dimensional attributes such as location. The forecasting calculation process for hierarchical energy systems can be especially complex because data and entities across hierarchical levels are involved, and it is typically desirable for forecasting to be consistent among them.